inst/MatchItShiny/ui.R
In BejaouiSayf/PbGit: An Interactive Visualisation And Performing Impacts Analysis
# Tu dois mettre ton shiny sur le site de MIT https://opensource.org/licenses/MIT
# gettext("Smart Interactive Evaluation Impacts App")), style = "color:#6E6E6E", align = "center")
# S : Exemple de titre pr l'app' !
data = c(
"Lalonde [1986]" = "lalondeMatchIt"
, "Lalonde Matching" = "lalondeMatching"
, "Upload your own..." = "file"
, "Green and Imai [2005]" = "GerberGreenImai"
, "Dehejia and Wahba [1999]" = "NSW_PSID"
)
sep = c(
Comma = ','
, Semicolon = ';'
, Tab = '\t'
, Space = ''
)
estimand = c(
"ATT (Sample Average Treatment effect for the Treated)" = "ATT"
, "ATE (Sample Average Treatment Effect)" = "ATE"
, "ATC (Sample Average Treatment Effect for the Controls)" = "ATC"
)
procedure = c(
"Propensity score matching (PSM), see for e.g Rosenbaum and Rubin (1983) " = "ps"
, "Exact Matching" = "exact"
, "Multivariate matching based on covariates" = "covarMatch"
# esk covarMatch = full Matching (Article Matching p5.)
, "Optimal full matching, see for e.g Hansen (2004); Hansen and Klopfer (2006); Rosenbaum (1989, 1991)" = "full"
# Je pense que l'optimal full matching est 1 variante de full matching.
, "Multivariate matching based on Mahalanobis distance, see for e.g Cochran and Rubin (1973); Rubin [1979, 1980])" = "mahalanobis"
, "Combination of Mahalanobis and PSM, see for e.g Rubin (2001); Rosenbaum and Rubin (1985))" = "mahalanobisPSM"
, "Genetic algorithm, see for e.g Diamond and Sekhon (2005) and Sekhon and Grieve (2011)" = "GenMatch"
, "CE Matching, see for e.g mettre les ref " = "cem"
, "Inverse variance" = "inversevarMatch"
)
distance = c(
"Mahalanobis distance" = "mahalanobis"
, "Logit (default)" = "logit"
, "Probit" = "probit"
, "Linear Logit" = "linear.logit"
, "Linear Probit" = "linear.probit"
, "log" = "log"
, "Linear log" = "linear.log"
, "Complementary log-log" = "cloglog"
, "Linear Complementary log-log" = "linear.cloglog"
, "Cauchit" = "cauchit"
, "Linear Cauchit" = "linearcauchit"
)
selectInput.distance.label <- em(h5("Select the model to estimate the propensity score or use Mahalanobis distance"))
selectInput.algo.subclass.distance = selectInput(
"algo.subclass.distance"
, selectInput.distance.label
, choices = distance
, selected = "logit"
)
selectInput.distance = selectInput(
"distance"
, selectInput.distance.label
, choices = distance
, selected = "logit"
)
method = c(
"Exact Matching" = "exact"
, "Subclassification" = "subclass"
, "Nearest neighbor(s) [default: KNN = 1]" = "nearest"
, "Optimal Matching" = "optimal"
, "Full Matching" = "full"
, "Genetic Matching" = "genetic"
, "Coarsened Exact Matching" = "cem"
)
Weight = c("1" = 1, "2" = 2, "3" = 3)
discard = c(
# "Without discarding" = "none"
# , "Hull both" = "hull.both"
# , "Hull Control" = "hull.control"
"Control" = "control"
# , "Hull treat" = "hull.treat"
, "Treat" = "treat"
, "Both" = "both"
)
numericInput.subclass = numericInput("subclass", em(h5("How many subclass (min = 2)?")), min = 2, value = 2)
numericInput.algo.subclass.subclass = numericInput("subclassrestriction", em(h5("How many subclass (default = 6, min = 2)?")), min = 2, value = 6)
fluidPage(titlePanel("eval")
, sidebarLayout(
sidebarPanel(
wellPanel(
div(align = "center", selectInput(
"data"
, ("Choose a dataset or upload your own")
, choices = data
)
, conditionalPanel(
tags$hr()
, condition = "input.data == 'file'"
, fileInput(
"file"
, "Choose your file"
)
, tags$hr()
, checkboxInput(
"header"
, "Header"
, TRUE
)
, checkboxInput(
"SEP"
, "Separator (tab, space,..., comma = default)"
, FALSE
)
, conditionalPanel(
condition="input.SEP==true"
, shiny::radioButtons(
"sep"
, ""
, choices = sep
, inline = TRUE
)
)
, checkboxInput(
"stringsAsFactors"
, "Strings as factors"
, FALSE
)
)
)
# Tu peux faire qq chose d'automatic qui detecte le type de fichier et telecharge la lib adéquate (foreign) pr 1fichier Stata, SAS...
# Sinon, tu peux obliger le User de convertir son fichier en csv pr qu'il soit exploitable
# Tu dois faire en sorte que l'app shiny puisse lire les fichiers ds l'environnement globale de R.
, wellPanel(
div(align = "center", checkboxInput("evalparam", strong("Show evaluation parameters"), FALSE)) # div ici est obligatoire
, conditionalPanel(
condition="input.evalparam==true"
, wellPanel(
div(align = "center", checkboxInput("varselec", "Modify variables selection", FALSE)
, conditionalPanel(
condition = "input.varselec == true"
, br()
, fluidRow(column(6
, uiOutput("outcome_var"))
, column(6
, uiOutput("treatment_var"))
, uiOutput("covariates")
# , selectInput(
# "Tr"
# , em(h5("Treatment variable"))
# , choices =
# )))
# , selectInput(
# "X"
# , em(h5("Move the covariates (that you don't need)"))
# , choices = NULL
# , multiple = TRUE
# , selectize = TRUE
)
)
)
)
, wellPanel(
div(align = "center", checkboxInput("Estimand", "Select the quantity of interest (ATT = default)", FALSE) # div ici est obligatoire
, conditionalPanel(
condition="input.Estimand==true"
, selectInput(
"estimand"
, ""
, choices = estimand
))))
, wellPanel(
div(align = "center", checkboxInput("otheralgo", "Perform other algorithms (PSM = default)", FALSE)
, conditionalPanel(
condition="input.otheralgo==true"
, selectInput(
"method"
, ""
, choices = method
, selected = "nearest"
)
# Options for sublassification method
, conditionalPanel(
condition = "input.method == 'subclass'"
, br()
, checkboxInput(
"opsubclass"
, em("Customize options")
, FALSE
)
, br()
, conditionalPanel(condition = "input.opsubclass == true"
, selectInput.algo.subclass.distance
, numericInput.algo.subclass.subclass
# , numericInput("subclass.subclass", em(h5("How many subclass?")), min = 2, value = 6)
)
# Il te manque de faire uploader un vecteur compris entre 0 et 1 pr le calcul des subclass
# either a scalar specifying the number of subclasses, or a vector of probabilities
# bounded between 0 and 1, which create quantiles of the distance measure using
# the units in the group specified by sub.by (default = subclass = 6).
# , tu ne va pas faire sub.by():critere qui indique le nbre de treat, de control ou all.
)
# Options for nearest neighbor method
, conditionalPanel(
# condition = "input.method != ''"
condition = "input.method != 'subclass'"
, checkboxInput(
"opnearest"
, em("Customize options")
, FALSE
)
, conditionalPanel(condition = "input.opnearest == true"
# , hr()
, selectInput.distance
, fluidRow(column(7
, numericInput("ratio"
, em(h5("How many control units to match to each treated unit?"))
, value = 1, min = 1
))
, column(5
, numericInput(
"caliper"
, em(h5("Caliper (example: 0.25)"))
, value = 0, min = 0
))
)
, fluidRow(
div(align = "left",
column(2
, checkboxInput(
"replace"
, em("Replacement?")
, FALSE
)
)
)
, div(align = "right", column(10
, checkboxInput(
"Discard"
, em("Discard units?")
, FALSE
)
)
)
)
, conditionalPanel(
condition = "input.Discard == true"
, paste("From:")
, shiny::radioButtons(
"discard"
, ""
, choices = discard
, selected = character(0)
, inline = TRUE
)
)
, hr()
, checkboxInput(
"moreop"
, em("See additional options?")
, FALSE
)
, conditionalPanel(
condition="input.moreop==true"
, br()
, checkboxInput(
"Subclass"
, "Do you want to perform Subclassifi-cation restriction?"
, FALSE
)
, conditionalPanel(
condition="input.Subclass==true"
, numericInput.subclass
)
, checkboxInput(
"Exact"
, "Do you want to perform Exact restriction?"
, FALSE
)
, conditionalPanel(
condition="input.Exact==true"
, selectInput(
"XExact"
, em(h5("Please select variable(s) on which you want to perform exact matching"))
, choices = NULL
, multiple = TRUE
)
)
, br()
, checkboxInput(
"caliperop"
, "Do you want to perform Mahalanobis-metric matching within each caliper?"
, FALSE
)
, conditionalPanel(
condition = "input.caliperop == true"
, selectInput(
"mahvars"
, em(h5("Please select variable(s) on which you want to perform Mahalanobis-metric"))
, choices = NULL
, multiple = TRUE
)
)
)
# )
# )
)
)
, conditionalPanel(
condition = "input.method == 'GenMatch'"
, checkboxInput(
"GenMatchWithPS"
, "The GenMatch is improved when a propensity score is incorporated. Hit if you want to add ps to the covariates."
)
, checkboxInput(
"parallelize"
, "Do you want to speed the process by parallelizing the Genetic algorithm. You can make use the multiple CPUs or a cluster of computers"
)
)
, hr()
, div(style = "position:relative", align = "center", actionButton("help_method", "Help", width = "23%", icon = icon("info-circle")))
)
)
)
)
)
, wellPanel(
div(align = "center", checkboxInput("results", strong("Save the results and graphs"), FALSE))
, conditionalPanel(condition = "input.results == true"
, br()
, div(align = "center", checkboxInput("graphop", "Show graphs options", FALSE))
, conditionalPanel(
condition="input.graphop==true"
, selectInput(
"type"
, "Optional: Select the type of the graph"
, choices = c("Histogram" = "hist", "Jitter" = "jitter")
, selected = "hist"
)
)
, h5(gettext("Save graphs"), align="center")
, shiny::radioButtons("paramdown","",
choices=list("PNG"="png","JPG"="jpg","PDF"="pdf")
,inline=TRUE
,selected="png"
)
)
)
, br()
, div(align="center",actionButton("PCAcode", gettext("Get the evaluation code")))
, br()
, div(align="center",actionButton("Quit", gettext("Quit the app")))
)
) # fin de parentèse de la 1è div.
, mainPanel(
tabsetPanel(id = "tabsetPanel"
, selected = ".Data"
#########################################################################
##### 7th tabPanel "Quick evaluation" #####
#########################################################################
, tabPanel("Quick Evaluation", icon = icon("heart")
, br()
, br()
, div(align = "center"
, strong(paste("The model specification: "), verbatimTextOutput("Model")
, paste("The outcome of interest: "), verbatimTextOutput("outcome")
, fluidRow(
column(6
# , strong(paste("Calculating the average treatment effect for the treated")
, paste("méthode manuelle")
, verbatimTextOutput("manuelle")
, verbatimTextOutput("x.out")
, verbatimTextOutput("x1.out")
, verbatimTextOutput("s.out.robuste")
)
, column(6
, paste("estimateur naif")
, verbatimTextOutput("naif")
, strong(paste("Matching"), verbatimTextOutput("matching")
, verbatimTextOutput("s.out")
, verbatimTextOutput("x1.out.robuste")
, verbatimTextOutput("z.out.robuste")
)
)
)
, br()
, br()
, paste("How many units where matched, unmatched or discarded")
, verbatimTextOutput("matchit_sample_sizes")
, br()
, br()
, paste("Remember you need to achieve satisfactory balance before saving the results!")
, br()
, br()
, plotOutput("graph")
# , strong(paste("Output of parameter of interest estimation", ""))
) # Fin de la () de strong
) # Fin de la () de la div
) # Fin de la () du tabPanel
#########################################################################
##### 6th tabPanel Balancing condition #####
#########################################################################
, tabPanel("Balancing condition", icon = icon("balance-scale")
# Rq : Autre noms possibles : Checking balance, Measures of the balance, Balance Statistics, Balance diagnostics, Assess balance !
, br()
, br()
, div(align = "center"
, fluidRow(
column(5
, strong(p("Samples sizes")
, verbatimTextOutput("matchit_summary_sample_sizes"))
, actionButton("help_goal_balance", "Why you need the best balance?", width = "72%", icon = icon("balance-scale")))
, column(5
, strong(p("Percent Balance Improvement")
, verbatimTextOutput("matchit_summary_percent_balance_improvement"))
, actionButton("help_percent_balance_improvement", "Mean Diff., eQQ...?", width = "45%", icon = icon("calculator")))
)
, br()
, hr()
, plotOutput("graph_balance")
, br()
, hr()
, fluidRow(
strong(p("Do you want to display additional results for checking balance?"))
, column(5
, checkboxInput(
"matchit_summary_balance_for_all_data"
, strong("Summary of balance for all data")
, FALSE
)
, verbatimTextOutput("matchit_summary_balance_for_all_data")
)
, column(5
, checkboxInput(
"matchit_summary_balance_for_matched_data"
, strong("Summary of balance for matched data")
, FALSE
)
, verbatimTextOutput("matchit_summary_balance_for_matched_data")
)
, div(align = "center", actionButton("help_all_matched_balance", "Help", width = "17%", icon = icon("info-circle")))
)
)
)
# , strong(paste("Output of parameter of interest estimation"), verbatimTextOutput("mb"))
# , strong(paste("Output Matchit Summary"), verbatimTextOutput("m.outSummary"))
# , verbatimTextOutput("matchit_summary")
#########################################################################
##### 5th tabPanel "Values" #####
#########################################################################
, tabPanel("Values", icon = icon("sort-numeric-asc")
, br()
, br()
, div(align = "center"
, uiOutput("Values")
, strong(verbatimTextOutput("results0dim"))
, strong(verbatimTextOutput("results0"))
, strong(verbatimTextOutput("results"))
, verbatimTextOutput("odds.ratios")
, plotOutput("resultsplot")
, fluidRow(
column(6
)
, column(6
, strong(paste("Summary of Propensity score"), verbatimTextOutput("distance")
, paste("Percent correctly predicted values"), verbatimTextOutput("MatriceConfusion")
)
)
)
, plotOutput("distancehistggplot")
, plotOutput("distancehist")
, plotOutput("distancecurve")
, verbatimTextOutput("mb")
, verbatimTextOutput("values")
)
)
#########################################################################
##### 4th tabPanel: Display interactive comparaison treat vs. control #####
#########################################################################
, tabPanel("Comparaison treat vs. control", icon = icon("search-plus")
, br()
, br()
, fluidRow(
column(5, uiOutput("var_single_tab3"))
, column(5, uiOutput("var_multi_tab3"))
)
, verbatimTextOutput("summaryByTreat")
# , verbatimTextOutput("summaryByTreatDF")
, p(downloadButton("download_summaryByTreat", "Download"), align = "center")
)
# the the summary
# , selectInput("bam", h6(gettext("Graphs for")), choices=list(IdChoices=VariableChoices),multiple=FALSE), plotOutput("histo")),
# Tu peux faire com ds Factoshiny 1menu déroulant pr voir le graph de chak var.
#########################################################################
##### 3rd tabPanel "Summary" #####
#########################################################################
## 3rd tabPanel: Display interactive summaries (more better than the default stats::summary and questionr::describe)
, tabPanel("Summary", icon = icon("bar-chart")
, br()
, br()
, uiOutput("VARS")
, dataTableOutput("data.selec.summary")
, verbatimTextOutput("download_summary_text")
, p(downloadButton("download_summary", "Download"), align = "center")
, br()
, br()
, uiOutput("VAR")
, plotOutput("data.selec.graph")
)
#########################################################################
##### 2sd tabPanel "Matched Data" #####
#########################################################################
, tabPanel(".Data", icon = icon("files-o"), br(), br()
, div(align = "center"
, shiny::radioButtons(
"m.data"
, "Obtain and download the generated data for the:"
, choices = c(
"Matched treatment group" = "treat"
, "Matched control group" = "control"
, "Unmatched data" = "unmatched"
, "Matched data" = "all"
# , "All" = "full"
)
, selected = "treat"
, inline = TRUE
)
, br()
, br()
, dataTableOutput({"m.data"})
# , tableOutput("m.data")
, br()
, br()
, strong(verbatimTextOutput("dim.m.data")
, downloadButton("download.m.data", "Download")
)
)
)
#########################################################################
##### 1st tabPanel "Data" #####
#########################################################################
## 1st tabPanel: Return dataframe (reactive version) and display dim
, tabPanel("Data", icon = icon("table"), br(), br()
, div(align = "center"
, tableOutput("data")
, br()
, strong(verbatimTextOutput("dim"))
, strong(print(em("This is your original database")),"!"))
)
# fluidRow(
# br(),
# column(width = 6,
# br(),
# conditionalPanel(
# condition="input.paramdown=='jpg'",
# p(downloadButton("downloadData4", gettext("Download as jpg")), align="center")),
# # conditionalPanel(
# condition="input.paramdown=='png'",
# p(downloadButton("downloadData3",gettext("Download as png")),align="center")),
# conditionalPanel(
# condition="input.paramdown=='pdf'",
# p(downloadButton("downloadData5",gettext("Download as pdf")),align="center")),
# br(),
# align="center")
# fluidRow(
# )
# , verbatimTextOutput("treatment_var")
# , verbatimTextOutput("covar")
)
)
)
)
BejaouiSayf/PbGit documentation built on May 28, 2019, 11:57 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Tu dois mettre ton shiny sur le site de MIT https://opensource.org/licenses/MIT
# gettext("Smart Interactive Evaluation Impacts App")), style = "color:#6E6E6E", align = "center")
# S : Exemple de titre pr l'app' !
data = c(
"Lalonde [1986]" = "lalondeMatchIt"
, "Lalonde Matching" = "lalondeMatching"
, "Upload your own..." = "file"
, "Green and Imai [2005]" = "GerberGreenImai"
, "Dehejia and Wahba [1999]" = "NSW_PSID"
)
sep = c(
Comma = ','
, Semicolon = ';'
, Tab = '\t'
, Space = ''
)
estimand = c(
"ATT (Sample Average Treatment effect for the Treated)" = "ATT"
, "ATE (Sample Average Treatment Effect)" = "ATE"
, "ATC (Sample Average Treatment Effect for the Controls)" = "ATC"
)
procedure = c(
"Propensity score matching (PSM), see for e.g Rosenbaum and Rubin (1983) " = "ps"
, "Exact Matching" = "exact"
, "Multivariate matching based on covariates" = "covarMatch"
# esk covarMatch = full Matching (Article Matching p5.)
, "Optimal full matching, see for e.g Hansen (2004); Hansen and Klopfer (2006); Rosenbaum (1989, 1991)" = "full"
# Je pense que l'optimal full matching est 1 variante de full matching.
, "Multivariate matching based on Mahalanobis distance, see for e.g Cochran and Rubin (1973); Rubin [1979, 1980])" = "mahalanobis"
, "Combination of Mahalanobis and PSM, see for e.g Rubin (2001); Rosenbaum and Rubin (1985))" = "mahalanobisPSM"
, "Genetic algorithm, see for e.g Diamond and Sekhon (2005) and Sekhon and Grieve (2011)" = "GenMatch"
, "CE Matching, see for e.g mettre les ref " = "cem"
, "Inverse variance" = "inversevarMatch"
)
distance = c(
"Mahalanobis distance" = "mahalanobis"
, "Logit (default)" = "logit"
, "Probit" = "probit"
, "Linear Logit" = "linear.logit"
, "Linear Probit" = "linear.probit"
, "log" = "log"
, "Linear log" = "linear.log"
, "Complementary log-log" = "cloglog"
, "Linear Complementary log-log" = "linear.cloglog"
, "Cauchit" = "cauchit"
, "Linear Cauchit" = "linearcauchit"
)
selectInput.distance.label <- em(h5("Select the model to estimate the propensity score or use Mahalanobis distance"))
selectInput.algo.subclass.distance = selectInput(
"algo.subclass.distance"
, selectInput.distance.label
, choices = distance
, selected = "logit"
)
selectInput.distance = selectInput(
"distance"
, selectInput.distance.label
, choices = distance
, selected = "logit"
)
method = c(
"Exact Matching" = "exact"
, "Subclassification" = "subclass"
, "Nearest neighbor(s) [default: KNN = 1]" = "nearest"
, "Optimal Matching" = "optimal"
, "Full Matching" = "full"
, "Genetic Matching" = "genetic"
, "Coarsened Exact Matching" = "cem"
)
Weight = c("1" = 1, "2" = 2, "3" = 3)
discard = c(
# "Without discarding" = "none"
# , "Hull both" = "hull.both"
# , "Hull Control" = "hull.control"
"Control" = "control"
# , "Hull treat" = "hull.treat"
, "Treat" = "treat"
, "Both" = "both"
)
numericInput.subclass = numericInput("subclass", em(h5("How many subclass (min = 2)?")), min = 2, value = 2)
numericInput.algo.subclass.subclass = numericInput("subclassrestriction", em(h5("How many subclass (default = 6, min = 2)?")), min = 2, value = 6)
fluidPage(titlePanel("eval")
, sidebarLayout(
sidebarPanel(
wellPanel(
div(align = "center", selectInput(
"data"
, ("Choose a dataset or upload your own")
, choices = data
)
, conditionalPanel(
tags$hr()
, condition = "input.data == 'file'"
, fileInput(
"file"
, "Choose your file"
)
, tags$hr()
, checkboxInput(
"header"
, "Header"
, TRUE
)
, checkboxInput(
"SEP"
, "Separator (tab, space,..., comma = default)"
, FALSE
)
, conditionalPanel(
condition="input.SEP==true"
, shiny::radioButtons(
"sep"
, ""
, choices = sep
, inline = TRUE
)
)
, checkboxInput(
"stringsAsFactors"
, "Strings as factors"
, FALSE
)
)
)
# Tu peux faire qq chose d'automatic qui detecte le type de fichier et telecharge la lib adéquate (foreign) pr 1fichier Stata, SAS...
# Sinon, tu peux obliger le User de convertir son fichier en csv pr qu'il soit exploitable
# Tu dois faire en sorte que l'app shiny puisse lire les fichiers ds l'environnement globale de R.
, wellPanel(
div(align = "center", checkboxInput("evalparam", strong("Show evaluation parameters"), FALSE)) # div ici est obligatoire
, conditionalPanel(
condition="input.evalparam==true"
, wellPanel(
div(align = "center", checkboxInput("varselec", "Modify variables selection", FALSE)
, conditionalPanel(
condition = "input.varselec == true"
, br()
, fluidRow(column(6
, uiOutput("outcome_var"))
, column(6
, uiOutput("treatment_var"))
, uiOutput("covariates")
# , selectInput(
# "Tr"
# , em(h5("Treatment variable"))
# , choices =
# )))
# , selectInput(
# "X"
# , em(h5("Move the covariates (that you don't need)"))
# , choices = NULL
# , multiple = TRUE
# , selectize = TRUE
)
)
)
)
, wellPanel(
div(align = "center", checkboxInput("Estimand", "Select the quantity of interest (ATT = default)", FALSE) # div ici est obligatoire
, conditionalPanel(
condition="input.Estimand==true"
, selectInput(
"estimand"
, ""
, choices = estimand
))))
, wellPanel(
div(align = "center", checkboxInput("otheralgo", "Perform other algorithms (PSM = default)", FALSE)
, conditionalPanel(
condition="input.otheralgo==true"
, selectInput(
"method"
, ""
, choices = method
, selected = "nearest"
)
# Options for sublassification method
, conditionalPanel(
condition = "input.method == 'subclass'"
, br()
, checkboxInput(
"opsubclass"
, em("Customize options")
, FALSE
)
, br()
, conditionalPanel(condition = "input.opsubclass == true"
, selectInput.algo.subclass.distance
, numericInput.algo.subclass.subclass
# , numericInput("subclass.subclass", em(h5("How many subclass?")), min = 2, value = 6)
)
# Il te manque de faire uploader un vecteur compris entre 0 et 1 pr le calcul des subclass
# either a scalar specifying the number of subclasses, or a vector of probabilities
# bounded between 0 and 1, which create quantiles of the distance measure using
# the units in the group specified by sub.by (default = subclass = 6).
# , tu ne va pas faire sub.by():critere qui indique le nbre de treat, de control ou all.
)
# Options for nearest neighbor method
, conditionalPanel(
# condition = "input.method != ''"
condition = "input.method != 'subclass'"
, checkboxInput(
"opnearest"
, em("Customize options")
, FALSE
)
, conditionalPanel(condition = "input.opnearest == true"
# , hr()
, selectInput.distance
, fluidRow(column(7
, numericInput("ratio"
, em(h5("How many control units to match to each treated unit?"))
, value = 1, min = 1
))
, column(5
, numericInput(
"caliper"
, em(h5("Caliper (example: 0.25)"))
, value = 0, min = 0
))
)
, fluidRow(
div(align = "left",
column(2
, checkboxInput(
"replace"
, em("Replacement?")
, FALSE
)
)
)
, div(align = "right", column(10
, checkboxInput(
"Discard"
, em("Discard units?")
, FALSE
)
)
)
)
, conditionalPanel(
condition = "input.Discard == true"
, paste("From:")
, shiny::radioButtons(
"discard"
, ""
, choices = discard
, selected = character(0)
, inline = TRUE
)
)
, hr()
, checkboxInput(
"moreop"
, em("See additional options?")
, FALSE
)
, conditionalPanel(
condition="input.moreop==true"
, br()
, checkboxInput(
"Subclass"
, "Do you want to perform Subclassifi-cation restriction?"
, FALSE
)
, conditionalPanel(
condition="input.Subclass==true"
, numericInput.subclass
)
, checkboxInput(
"Exact"
, "Do you want to perform Exact restriction?"
, FALSE
)
, conditionalPanel(
condition="input.Exact==true"
, selectInput(
"XExact"
, em(h5("Please select variable(s) on which you want to perform exact matching"))
, choices = NULL
, multiple = TRUE
)
)
, br()
, checkboxInput(
"caliperop"
, "Do you want to perform Mahalanobis-metric matching within each caliper?"
, FALSE
)
, conditionalPanel(
condition = "input.caliperop == true"
, selectInput(
"mahvars"
, em(h5("Please select variable(s) on which you want to perform Mahalanobis-metric"))
, choices = NULL
, multiple = TRUE
)
)
)
# )
# )
)
)
, conditionalPanel(
condition = "input.method == 'GenMatch'"
, checkboxInput(
"GenMatchWithPS"
, "The GenMatch is improved when a propensity score is incorporated. Hit if you want to add ps to the covariates."
)
, checkboxInput(
"parallelize"
, "Do you want to speed the process by parallelizing the Genetic algorithm. You can make use the multiple CPUs or a cluster of computers"
)
)
, hr()
, div(style = "position:relative", align = "center", actionButton("help_method", "Help", width = "23%", icon = icon("info-circle")))
)
)
)
)
)
, wellPanel(
div(align = "center", checkboxInput("results", strong("Save the results and graphs"), FALSE))
, conditionalPanel(condition = "input.results == true"
, br()
, div(align = "center", checkboxInput("graphop", "Show graphs options", FALSE))
, conditionalPanel(
condition="input.graphop==true"
, selectInput(
"type"
, "Optional: Select the type of the graph"
, choices = c("Histogram" = "hist", "Jitter" = "jitter")
, selected = "hist"
)
)
, h5(gettext("Save graphs"), align="center")
, shiny::radioButtons("paramdown","",
choices=list("PNG"="png","JPG"="jpg","PDF"="pdf")
,inline=TRUE
,selected="png"
)
)
)
, br()
, div(align="center",actionButton("PCAcode", gettext("Get the evaluation code")))
, br()
, div(align="center",actionButton("Quit", gettext("Quit the app")))
)
) # fin de parentèse de la 1è div.
, mainPanel(
tabsetPanel(id = "tabsetPanel"
, selected = ".Data"
#########################################################################
##### 7th tabPanel "Quick evaluation" #####
#########################################################################
, tabPanel("Quick Evaluation", icon = icon("heart")
, br()
, br()
, div(align = "center"
, strong(paste("The model specification: "), verbatimTextOutput("Model")
, paste("The outcome of interest: "), verbatimTextOutput("outcome")
, fluidRow(
column(6
# , strong(paste("Calculating the average treatment effect for the treated")
, paste("méthode manuelle")
, verbatimTextOutput("manuelle")
, verbatimTextOutput("x.out")
, verbatimTextOutput("x1.out")
, verbatimTextOutput("s.out.robuste")
)
, column(6
, paste("estimateur naif")
, verbatimTextOutput("naif")
, strong(paste("Matching"), verbatimTextOutput("matching")
, verbatimTextOutput("s.out")
, verbatimTextOutput("x1.out.robuste")
, verbatimTextOutput("z.out.robuste")
)
)
)
, br()
, br()
, paste("How many units where matched, unmatched or discarded")
, verbatimTextOutput("matchit_sample_sizes")
, br()
, br()
, paste("Remember you need to achieve satisfactory balance before saving the results!")
, br()
, br()
, plotOutput("graph")
# , strong(paste("Output of parameter of interest estimation", ""))
) # Fin de la () de strong
) # Fin de la () de la div
) # Fin de la () du tabPanel
#########################################################################
##### 6th tabPanel Balancing condition #####
#########################################################################
, tabPanel("Balancing condition", icon = icon("balance-scale")
# Rq : Autre noms possibles : Checking balance, Measures of the balance, Balance Statistics, Balance diagnostics, Assess balance !
, br()
, br()
, div(align = "center"
, fluidRow(
column(5
, strong(p("Samples sizes")
, verbatimTextOutput("matchit_summary_sample_sizes"))
, actionButton("help_goal_balance", "Why you need the best balance?", width = "72%", icon = icon("balance-scale")))
, column(5
, strong(p("Percent Balance Improvement")
, verbatimTextOutput("matchit_summary_percent_balance_improvement"))
, actionButton("help_percent_balance_improvement", "Mean Diff., eQQ...?", width = "45%", icon = icon("calculator")))
)
, br()
, hr()
, plotOutput("graph_balance")
, br()
, hr()
, fluidRow(
strong(p("Do you want to display additional results for checking balance?"))
, column(5
, checkboxInput(
"matchit_summary_balance_for_all_data"
, strong("Summary of balance for all data")
, FALSE
)
, verbatimTextOutput("matchit_summary_balance_for_all_data")
)
, column(5
, checkboxInput(
"matchit_summary_balance_for_matched_data"
, strong("Summary of balance for matched data")
, FALSE
)
, verbatimTextOutput("matchit_summary_balance_for_matched_data")
)
, div(align = "center", actionButton("help_all_matched_balance", "Help", width = "17%", icon = icon("info-circle")))
)
)
)
# , strong(paste("Output of parameter of interest estimation"), verbatimTextOutput("mb"))
# , strong(paste("Output Matchit Summary"), verbatimTextOutput("m.outSummary"))
# , verbatimTextOutput("matchit_summary")
#########################################################################
##### 5th tabPanel "Values" #####
#########################################################################
, tabPanel("Values", icon = icon("sort-numeric-asc")
, br()
, br()
, div(align = "center"
, uiOutput("Values")
, strong(verbatimTextOutput("results0dim"))
, strong(verbatimTextOutput("results0"))
, strong(verbatimTextOutput("results"))
, verbatimTextOutput("odds.ratios")
, plotOutput("resultsplot")
, fluidRow(
column(6
)
, column(6
, strong(paste("Summary of Propensity score"), verbatimTextOutput("distance")
, paste("Percent correctly predicted values"), verbatimTextOutput("MatriceConfusion")
)
)
)
, plotOutput("distancehistggplot")
, plotOutput("distancehist")
, plotOutput("distancecurve")
, verbatimTextOutput("mb")
, verbatimTextOutput("values")
)
)
#########################################################################
##### 4th tabPanel: Display interactive comparaison treat vs. control #####
#########################################################################
, tabPanel("Comparaison treat vs. control", icon = icon("search-plus")
, br()
, br()
, fluidRow(
column(5, uiOutput("var_single_tab3"))
, column(5, uiOutput("var_multi_tab3"))
)
, verbatimTextOutput("summaryByTreat")
# , verbatimTextOutput("summaryByTreatDF")
, p(downloadButton("download_summaryByTreat", "Download"), align = "center")
)
# the the summary
# , selectInput("bam", h6(gettext("Graphs for")), choices=list(IdChoices=VariableChoices),multiple=FALSE), plotOutput("histo")),
# Tu peux faire com ds Factoshiny 1menu déroulant pr voir le graph de chak var.
#########################################################################
##### 3rd tabPanel "Summary" #####
#########################################################################
## 3rd tabPanel: Display interactive summaries (more better than the default stats::summary and questionr::describe)
, tabPanel("Summary", icon = icon("bar-chart")
, br()
, br()
, uiOutput("VARS")
, dataTableOutput("data.selec.summary")
, verbatimTextOutput("download_summary_text")
, p(downloadButton("download_summary", "Download"), align = "center")
, br()
, br()
, uiOutput("VAR")
, plotOutput("data.selec.graph")
)
#########################################################################
##### 2sd tabPanel "Matched Data" #####
#########################################################################
, tabPanel(".Data", icon = icon("files-o"), br(), br()
, div(align = "center"
, shiny::radioButtons(
"m.data"
, "Obtain and download the generated data for the:"
, choices = c(
"Matched treatment group" = "treat"
, "Matched control group" = "control"
, "Unmatched data" = "unmatched"
, "Matched data" = "all"
# , "All" = "full"
)
, selected = "treat"
, inline = TRUE
)
, br()
, br()
, dataTableOutput({"m.data"})
# , tableOutput("m.data")
, br()
, br()
, strong(verbatimTextOutput("dim.m.data")
, downloadButton("download.m.data", "Download")
)
)
)
#########################################################################
##### 1st tabPanel "Data" #####
#########################################################################
## 1st tabPanel: Return dataframe (reactive version) and display dim
, tabPanel("Data", icon = icon("table"), br(), br()
, div(align = "center"
, tableOutput("data")
, br()
, strong(verbatimTextOutput("dim"))
, strong(print(em("This is your original database")),"!"))
)
# fluidRow(
# br(),
# column(width = 6,
# br(),
# conditionalPanel(
# condition="input.paramdown=='jpg'",
# p(downloadButton("downloadData4", gettext("Download as jpg")), align="center")),
# # conditionalPanel(
# condition="input.paramdown=='png'",
# p(downloadButton("downloadData3",gettext("Download as png")),align="center")),
# conditionalPanel(
# condition="input.paramdown=='pdf'",
# p(downloadButton("downloadData5",gettext("Download as pdf")),align="center")),
# br(),
# align="center")
# fluidRow(
# )
# , verbatimTextOutput("treatment_var")
# , verbatimTextOutput("covar")
)
)
)
)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.